ABSTRACT Systemic lupus erythematosus (SLE or lupus) is a complex, heterogeneous autoimmune disease that results in inflammation and systemic end-organ damage. Previous genome-wide association studies (GWAS) using high-density single nucleotide polymorphism (SNP) arrays have been successful in identifying >20 associations between common genetic variations (minor allele frequency (MAF) > 5%) and SLE. These loci, however, only account for a small proportion of SLE heritability and the causal variants tagged by these common variant (CV) associations are still unknown. These deficiencies, along with the advent of next generation sequencing platforms, have spurred our interest in exploring the contribution of rare variants (RV) to genetic susceptibility. The contribution of RVs to SLE susceptibility represents a critical and unmet need that is highly likely to identify important novel SLE genetic loci that current GWAS-based methods miss. For the renewal of AI063274, we will direct our efforts toward the identification and characterization of RVs by leveraging our recent progress in next-generation sequencing, concentrating on confirmed SLE risk loci and extending these studies, through exome resequencing, to loci that have escaped detection by GWAS. In addition, we have developed a novel, data driven analysis approach that will make use of the most powerful RV analysis methods for a variety of RV scenarios that we may encounter. Specifically, we propose to 1) identify RVs in established SLE risk genes through a targeted resequencing approach and quantify the contribution of RVs to the association signals marked by CVs, 2) comprehensively identify coding region RVs within the exome and quantify the extent to which they influence SLE susceptibility, and 3) perform replication resequencing in independent SLE cases and controls for regions demonstrating RV association to validate true positive RV effects. Our assembled team is the only one in the world currently conducting large-scale next generation sequencing in SLE. We have extensive and relevant experience in next generation sequencing and are well poised to leverage computational resources and sophisticated statistical expertise to ensure our success. We believe that understanding the role of RVs in the genetic architecture of SLE will result in the identification of important causal variants that influence SLE predisposition. When successfully completed, the data generated by this R01 will provide fundamental insights for functional studies and fine mapping efforts within regions of CV association and lead to important new hypotheses of autoimmune pathophysiology. !